Network slimming [1]} adds L1 regularization to the scale parameter in the BN layer, and finally employs the absolute value of the scale parameter as the important metric of the corresponding convolution kernel.
L1-norm pruning [2]} also adds L1 regularization to the weights of all convolution kernels and then uses the L1-norm of the convolution kernel as its importance metric.
To avoid the impact of adding additional L1 regularization on the performance of the generator and additional fine-tuning after pruning.
We only use 1/10 of the original training epoch to add the L1 regularization to the generator, and then sort all the convolution kernels in ascending order according to their importance metric.
Given computational constrain, the pruning method removes the convolution kernel with less importance until the requirements are met.
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